Advancements in Artificial Intelligence and Data Science for Cardiovascular Health
A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "TeleHealth and Digital Healthcare".
Deadline for manuscript submissions: 30 June 2024 | Viewed by 7079
Special Issue Editor
Interests: (genetic) epidemiology of cardiovascular diseases; big data; genome-wide association studies; genetic risk scores; mendelian randomization; machine learning
Special Issue Information
Dear Colleagues,
Cardiovascular disease is the highest cause of mortality worldwide. Recent advances in molecular epidemiological methods and techniques have remarkably enhanced cardiovascular health. Techniques such as genomics, metabolomics, proteomics, and transcriptomics have generated a large amount of data.
In addition, novel analytical approaches in genomics, such as genome-wide association analysis, genetic correlation, and gene-set enrichment analysis, have emerged in the last decade, improving our understanding of the biological pathways involved in cardiovascular diseases. Methods such as Mendelian randomization analysis and polygenic risk scores have improved our insight into causal factors. Machine learning approaches are increasingly being applied in the field and provide a better understanding of various predictors of cardiovascular diseases. In this issue, we are seeking to obtain insight into the advances that these methods and techniques have brought into the field.
The topics of interest include, but are not limited to, the following:
- Accelerating patient benefit in cardiology using artificial intelligence;
- Artificial intelligence for public health;
- Cardiac image processing;
- Data mining in cardiology;
- Decision support systems in cardiovascular health;
- Digital cardiology;
- Machine learning and deep learning methods in cardiovascular health;
- Machine learning in drug development in cardiology;
- Machine learning to handle cardiology hospital records;
- Personalised cardiology using machine learning;
- The prediction of cardiovascular risk (hypertension, myocardial infarction, atherosclerosis, stroke, etc.).
Dr. Raha Pazoki
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Healthcare is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- molecular epidemiology
- machine learning
- cardiovascular disease
- data science
